Update app.py

app.py

@@ -1,257 +1,305 @@
-import os
-import gradio as gr
-import nltk
-import numpy as np
-import tflearn
-import random
-import json
-import pickle
-from nltk.tokenize import word_tokenize
-from nltk.stem.lancaster import LancasterStemmer
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-import googlemaps
-import folium
-import torch
-
-# Suppress TensorFlow warnings
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
-# Download necessary NLTK resources
-nltk.download("punkt")
-stemmer = LancasterStemmer()
-
-# Load intents and chatbot training data
-with open("intents.json") as file:
-    intents_data = json.load(file)
-
-with open("data.pickle", "rb") as f:
-    words, labels, training, output = pickle.load(f)
-
-# Build the chatbot model
-net = tflearn.input_data(shape=[None, len(training[0])])
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
-net = tflearn.regression(net)
-chatbot_model = tflearn.DNN(net)
-chatbot_model.load("MentalHealthChatBotmodel.tflearn")
-
-# Hugging Face sentiment and emotion models
-tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-
-# Google Maps API Client
-gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
-
-# Helper Functions
-def bag_of_words(s, words):
-    """Convert user input to bag-of-words vector."""
-    bag = [0] * len(words)
-    s_words = word_tokenize(s)
-    s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
-    for se in s_words:
-        for i, w in enumerate(words):
-            if w == se:
-                bag[i] = 1
-    return np.array(bag)
-
-def generate_chatbot_response(message, history):
-    """Generate chatbot response and maintain conversation history."""
-    history = history or []
-    try:
-        result = chatbot_model.predict([bag_of_words(message, words)])
-        tag = labels[np.argmax(result)]
-        response = "I'm sorry, I didn't understand that. 🤔"
-        for intent in intents_data["intents"]:
-            if intent["tag"] == tag:
-                response = random.choice(intent["responses"])
-                break
-    except Exception as e:
-        response = f"Error: {e}"
-    history.append((message, response))
-    return history, response
-
-def analyze_sentiment(user_input):
-    """Analyze sentiment and map to emojis."""
-    inputs = tokenizer_sentiment(user_input, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model_sentiment(**inputs)
-    sentiment_class = torch.argmax(outputs.logits, dim=1).item()
-    sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
-    return f"Sentiment: {sentiment_map[sentiment_class]}"
-
-def detect_emotion(user_input):
-    """Detect emotions based on input."""
-    pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
-    result = pipe(user_input)
-    emotion = result[0]["label"].lower().strip()
-    emotion_map = {
-        "joy": "Joy 😊",
-        "anger": "Anger 😠",
-        "sadness": "Sadness 😢",
-        "fear": "Fear 😨",
-        "surprise": "Surprise 😲",
-        "neutral": "Neutral 😐",
-    }
-    return emotion_map.get(emotion, "Unknown 🤔"), emotion
-
-def generate_suggestions(emotion):
-    """Return relevant suggestions based on detected emotions."""
-    emotion_key = emotion.lower()
-    suggestions = {
-        "joy": [
-            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
-            ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
-        ],
-        "anger": [
-            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Stress Management Tips", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'],
-            ["Dealing with Anger", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
-        ],
-        "fear": [
-            ["Mindfulness Practices", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
-            ["Coping with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>'],
-        ],
-        "sadness": [
-            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Dealing with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>'],
-        ],
-        "surprise": [
-            ["Managing Stress", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'],
-            ["Coping Strategies", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
-        ],
-    }
-    return suggestions.get(emotion_key, [["No specific suggestions available.", ""]])
-
-def get_health_professionals_and_map(location, query):
-    """Search nearby healthcare professionals using Google Maps API."""
-    try:
-        if not location or not query:
-            return ["Please provide both location and query."], ""
-
-        geo_location = gmaps.geocode(location)
-        if geo_location:
-            lat, lng = geo_location[0]["geometry"]["location"].values()
-            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
-            professionals = []
-            map_ = folium.Map(location=(lat, lng), zoom_start=13)
-            for place in places_result:
-                professionals.append(f"{place['name']} - {place.get('vicinity', 'No address provided')}")
-                folium.Marker(
-                    location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
-                    popup=f"{place['name']}"
-                ).add_to(map_)
-            return professionals, map_._repr_html_()
-
-        return ["No professionals found for the given location."], ""
-    except Exception as e:
-        return [f"An error occurred: {e}"], ""
-
-# Main Application Logic
-def app_function(user_input, location, query, history):
-    chatbot_history, _ = generate_chatbot_response(user_input, history)
-    sentiment_result = analyze_sentiment(user_input)
-    emotion_result, cleaned_emotion = detect_emotion(user_input)
-    suggestions = generate_suggestions(cleaned_emotion)
-    professionals, map_html = get_health_professionals_and_map(location, query)
-    return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
-
-# CSS Styling
-custom_css = """
-body {
-    font-family: 'Roboto', sans-serif;
-    background: linear-gradient(135deg, #0d0d0d, #ff5722);
-    color: white;
-}
-
-h1 {
-    background: #ffffff;
-    color: #000000;
-    border-radius: 8px;
-    padding: 10px;
-    font-weight: bold;
-    text-align: center;
-    font-size: 2.5rem;
-}
-
-textarea, input {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-    padding: 8px;
-    font-size: 1rem;
-    caret-color: black;
-    outline: none;
-    border-radius: 8px;
-}
-
-textarea:focus, input:focus {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-    outline: none;
-}
-
-textarea:hover, input:hover {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-}
-
-.df-container {
-    background: white;
-    color: black;
-    border: 2px solid orange;
-    border-radius: 10px;
-    padding: 10px;
-    font-size: 14px;
-    max-height: 400px;
-    height: auto;
-    overflow-y: auto;
-}
-
-#suggestions-title {
-    text-align: center;
-    font-weight: bold;
-    color: white;
-    font-size: 2.2rem;
-    margin-bottom: 20px;
-}
-"""
-
-# Gradio Application
-with gr.Blocks(css=custom_css) as app:
-    gr.HTML("<h1>🌟 Well-Being Companion</h1>")
-    with gr.Row():
-        user_input = gr.Textbox(label="Your Message")
-        location = gr.Textbox(label="Your Location")
-        query = gr.Textbox(label="Search Query")
-    chatbot = gr.Chatbot(label="Chat History")
-    sentiment = gr.Textbox(label="Detected Sentiment")
-    emotion = gr.Textbox(label="Detected Emotion")
-    
-    # Adding Suggestions Title with Styled Markdown (Centered and Bold)
-    gr.Markdown("Suggestions", elem_id="suggestions-title")
-    
-    suggestions = gr.DataFrame(headers=["Title", "Link"])  # Table for suggestions
-    professionals = gr.Textbox(label="Nearby Professionals", lines=6)
-    map_html = gr.HTML(label="Interactive Map")
-    submit = gr.Button(value="Submit", variant="primary")
-
-    submit.click(
-        app_function,
-        inputs=[user_input, location, query, chatbot],
-        outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html],
-    )
-
-app.launch()
+import os
+import gradio as gr
+import nltk
+import numpy as np
+import json
+import pickle
+from nltk.tokenize import word_tokenize
+from nltk.stem.lancaster import LancasterStemmer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score
+
+# Suppress TensorFlow warnings
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# NLTK Setup
+nltk.download("punkt")
+stemmer = LancasterStemmer()
+
+# Load data
+with open("intents.json") as file:
+    intents_data = json.load(file)
+
+with open("data.pickle", "rb") as f:
+    words, labels, training, output = pickle.load(f)
+
+# Hugging Face models for Well-Being Companion
+tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+
+# Helper Functions
+def bag_of_words(s, words):
+    bag = [0] * len(words)
+    s_words = word_tokenize(s)
+    s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
+    for se in s_words:
+        for i, w in enumerate(words):
+            if w == se:
+                bag[i] = 1
+    return np.array(bag)
+def analyze_sentiment(user_input):
+    """Analyze sentiment and map to emojis."""
+    inputs = tokenizer_sentiment(user_input, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model_sentiment(**inputs)
+    sentiment_class = torch.argmax(outputs.logits, dim=1).item()
+    sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
+    return f"Sentiment: {sentiment_map[sentiment_class]}"
+
+def detect_emotion(user_input):
+    """Detect emotions based on input."""
+    pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
+    result = pipe(user_input)
+    emotion = result[0]["label"].lower().strip()
+    emotion_map = {
+        "joy": "Joy 😊",
+        "anger": "Anger 😠",
+        "sadness": "Sadness 😢",
+        "fear": "Fear 😨",
+        "surprise": "Surprise 😲",
+        "neutral": "Neutral 😐",
+    }
+    # Return only the formatted emotion string
+    return emotion_map.get(emotion, "Unknown 🤔")
+
+def generate_suggestions(emotion):
+    """Return relevant suggestions based on detected emotions."""
+    emotion_key = emotion.lower()
+    suggestions = {
+        "joy": [
+            ["Relaxation Techniques", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"],
+            ["Dealing with Stress", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"],
+        ],
+        "anger": [
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Stress Management Tips", "https://www.health.harvard.edu/health-a-to-z"],
+            ["Dealing with Anger", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Relaxation Video", "https://youtu.be/MIc299Flibs"],
+        ],
+        "fear": [
+            ["Mindfulness Practices", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"],
+            ["Coping with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Relaxation Video", "https://youtu.be/yGKKz185M5o"],
+        ],
+        "sadness": [
+            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
+            ["Dealing with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Relaxation Video", "https://youtu.be/-e-4Kx5px_I"],
+        ],
+        "surprise": [
+            ["Managing Stress", "https://www.health.harvard.edu/health-a-to-z"],
+            ["Coping Strategies", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
+            ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"],
+        ],
+    }
+    return suggestions.get(emotion_key, [["No specific suggestions available.", "#"]])
+
+def get_health_professionals_and_map(location, query):
+    """Search nearby healthcare professionals using Google Maps API."""
+    try:
+        if not location or not query:
+            return [], ""  # Return empty list if inputs are missing
+
+        geo_location = gmaps.geocode(location)
+        if geo_location:
+            lat, lng = geo_location[0]["geometry"]["location"].values()
+            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
+            professionals = []
+            map_ = folium.Map(location=(lat, lng), zoom_start=13)
+            for place in places_result:
+                # Use a list of values to append each professional
+                professionals.append([place['name'], place.get('vicinity', 'No address provided')])
+                folium.Marker(
+                    location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
+                    popup=f"{place['name']}"
+                ).add_to(map_)
+            return professionals, map_._repr_html_()
+
+        return [], ""  # Return empty list if no professionals found
+    except Exception as e:
+        return [], ""  # Return empty list on exception
+
+
+# Chronic Disease Prediction Functions
+def load_data():
+    df = pd.read_csv("Training.csv")
+    tr = pd.read_csv("Testing.csv")
+    disease_dict = {  'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
+        'Peptic ulcer diseae': 5, 'AIDS': 6, 'Diabetes ': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
+        'Hypertension ': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis (brain hemorrhage)': 13,
+        'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'hepatitis A': 19,
+        'Hepatitis B': 20, 'Hepatitis C': 21, 'Hepatitis D': 22, 'Hepatitis E': 23, 'Alcoholic hepatitis': 24,
+        'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemmorhoids(piles)': 28,
+        'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
+        'Hypoglycemia': 33, 'Osteoarthristis': 34, 'Arthritis': 35,
+        '(vertigo) Paroymsal  Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
+        'Psoriasis': 39, 'Impetigo': 40
+    } # Same logic.
+    df.replace({'prognosis': disease_dict}, inplace=True)
+    return df, tr, disease_dict
+
+df, tr, disease_dict = load_data()
+l1 = list(df.columns[:-1])
+X = df[l1]
+y = df['prognosis']
+X_test = tr[l1]
+y_test = tr['prognosis']
+
+def train_models():
+    models = {
+        "Decision Tree": DecisionTreeClassifier(),
+        "Random Forest": RandomForestClassifier(),
+        "Naive Bayes": GaussianNB()
+    }
+    trained_models = {}
+    for model_name, model_obj in models.items():
+        model_obj.fit(X, y)
+        acc = accuracy_score(y_test, model_obj.predict(X_test))
+        trained_models[model_name] = (model_obj, acc)
+    return trained_models
+
+trained_models = train_models()
+
+disease_to_professional = {
+    'Fungal infection': ["Dermatologist", "Family Doctor"],
+    'Allergy': ["Allergist", "Family Doctor"],
+    'GERD': ["Gastroenterologist", "Family Doctor"],
+    'Chronic cholestasis': ["Gastroenterologist", "Family Doctor"],
+    'Drug Reaction': ["Dermatologist", "Family Doctor"],
+    'Peptic ulcer disease': ["Gastroenterologist", "Family Doctor"],
+    'AIDS': ["Infectious Disease Specialist", "Family Doctor"],
+    'Diabetes ': ["Endocrinologist", "Family Doctor"],
+    'Gastroenteritis': ["Gastroenterologist", "Family Doctor"],
+    'Bronchial Asthma': ["Pulmonologist", "Family Doctor"],
+    'Hypertension ': ["Cardiologist", "Family Doctor"],
+    'Migraine': ["Neurologist", "Family Doctor"],
+    'Cervical spondylosis': ["Orthopedist", "Family Doctor"],
+    'Paralysis (brain hemorrhage)': ["Neurologist", "Family Doctor"],
+    'Jaundice': ["Hepatologist", "Family Doctor"],
+    'Malaria': ["Infectious Disease Specialist", "Family Doctor"],
+    'Chicken pox': ["Pediatrician", "Family Doctor"],
+    'Dengue': ["Infectious Disease Specialist", "Family Doctor"],
+    'Typhoid': ["Infectious Disease Specialist", "Family Doctor"],
+    'hepatitis A': ["Hepatologist", "Family Doctor"],
+    'Hepatitis B': ["Hepatologist", "Family Doctor"],
+    'Hepatitis C': ["Hepatologist", "Family Doctor"],
+    'Hepatitis D': ["Hepatologist", "Family Doctor"],
+    'Hepatitis E': ["Hepatologist", "Family Doctor"],
+    'Alcoholic hepatitis': ["Hepatologist", "Family Doctor"],
+    'Tuberculosis': ["Pulmonologist", "Family Doctor"],
+    'Common Cold': ["General Practitioner"],
+    'Pneumonia': ["Pulmonologist", "Family Doctor"],
+    'Dimorphic hemorrhoids(piles)': ["Gastroenterologist", "Family Doctor"],
+    'Heart attack': ["Cardiologist"],
+    'Varicose veins': ["Vascular Surgeon", "Family Doctor"],
+    'Hypothyroidism': ["Endocrinologist", "Family Doctor"],
+    'Hyperthyroidism': ["Endocrinologist", "Family Doctor"],
+    'Hypoglycemia': ["Endocrinologist", "Family Doctor"],
+    'Osteoarthritis': ["Orthopedist", "Family Doctor"],
+    'Arthritis': ["Rheumatologist", "Family Doctor"],
+    '(vertigo) Paroxysmal Positional Vertigo': ["Neurologist", "Family Doctor"],
+    'Acne': ["Cosmetic Dermatologist", "Family Doctor"],
+    'Urinary tract infection': ["Urologist", "Family Doctor"],
+    'Psoriasis': ["Dermatologist", "Family Doctor"],
+    'Impetigo': ["Dermatologist", "Family Doctor"]
+}
+def disease_predictor(symptoms):
+    results = []
+    
+    for model_name, (model, acc) in trained_models.items():
+        disease = predict_disease(model, symptoms)
+        pro = disease_to_professional.get(disease, ["No Recommendations Available"])
+        
+        # Convert the list of recommended professionals into a string without commas
+        pro_str = " and ".join(pro) if isinstance(pro, list) else pro
+        
+        results.append(f"Model: {model_name}\nPredicted Disease: {disease}\nRecommended Professionals: {pro_str}\n")
+    
+    return "\n".join(results)
+
+
+    
+def predict_disease_button(symptom1, symptom2, symptom3, symptom4, symptom5):
+    # Filter out "None" values and pass selected symptoms
+    selected_symptoms = [s for s in [symptom1, symptom2, symptom3, symptom4, symptom5] if s != "None"]
+    
+    # Check if at least 3 symptoms are selected
+    if len(selected_symptoms) < 3:
+        return "Please select at least three symptoms."
+    else:
+        return disease_predictor(selected_symptoms)
+
+# Gradio App 
+with gr.Blocks() as app:
+
+    with gr.Tab("Well-Being Companion"):
+        gr.Markdown("<h1>🌟 Well-Being Companion</h1><p>Track your health, mood, and more!</p>")
+        
+        with gr.Row():
+            user_input = gr.Textbox(label="Describe Your Current Feeling or Concern:", placeholder="How are you feeling today?")
+            location = gr.Textbox(label="Location", placeholder="e.g., New York")
+            query = gr.Textbox(label="Search for Professionals or Services", placeholder="e.g., therapist, dietitian.")
+        
+        with gr.Row():
+            sentiment_btn = gr.Button("Analyze Sentiment")
+            sentiment_result = gr.Textbox(label="Sentiment Analysis")
+            emotion_btn = gr.Button("Detect Emotion")
+            emotion_result = gr.Textbox(label="Emotion Detection")
+
+        # Set up click functionality
+        sentiment_btn.click(analyze_sentiment, inputs=user_input, outputs=sentiment_result)
+        emotion_btn.click(detect_emotion, inputs=user_input, outputs=emotion_result)
+        
+        gr.Markdown("### Suggestions", elem_id="suggestions-title")
+
+        # Table to display suggestions
+        suggestions_table = gr.DataFrame(headers=["Title", "Link"])
+
+        # New 'Get Suggestion' button
+        with gr.Row():
+            suggestion_btn = gr.Button("Get Suggestion")
+            suggestion_btn.click(generate_suggestions, inputs=emotion_result, outputs=suggestions_table)
+
+        with gr.Row():
+            nearby_btn = gr.Button("Find Nearby Professionals")
+            professionals_output = gr.Textbox(label="Professionals")
+
+        nearby_btn.click(get_health_professionals_and_map, inputs=[location, query], outputs=professionals_output)
+
+    with gr.Tab("Chat History"):
+        gr.Markdown("<h3>Chat History:</h3>")
+        chat_history = gr.Textbox(label="Chat Logs", placeholder="Conversation history will appear here.")
+
+    with gr.Tab("Chronic Disease Prediction"):
+        gr.Markdown("<h1>🩺 Chronic Disease Prediction</h1><p>Enter your symptoms to get a prediction.</p>")
+
+        symptom1 = gr.Dropdown(["None"] + l1, label="Symptom 1")
+        symptom2 = gr.Dropdown(["None"] + l1, label="Symptom 2")
+        symptom3 = gr.Dropdown(["None"] + l1, label="Symptom 3")
+        symptom4 = gr.Dropdown(["None"] + l1, label="Symptom 4")
+        symptom5 = gr.Dropdown(["None"] + l1, label="Symptom 5")
+
+        predict_button = gr.Button("Predict Disease")
+        prediction_result = gr.Textbox(label="Prediction Result")
+
+        predict_button.click(
+            fn=predict_disease_button,
+            inputs=[symptom1, symptom2, symptom3, symptom4, symptom5],
+            outputs=prediction_result
+        )
+
+# Launch the app
+app.launch()
+
+